Energy demand/Description: Difference between revisions

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In the generic model, demand for final energy is calculated for every region (R), sector (S) and energy form (F, heat or electricity) according to:
In the generic model, demand for final energy is calculated for every region (R), sector (S) and energy form (F, heat or electricity) according to:


[[File:Formula1_ED.PNG]]
[[File:Formula1_ED.PNG|left]]
[[File:Formula1_ED.PNG|left]]
 
 


in which SE represents final energy, POP represents population, ACT/POP the sectoral activity per capita, SC a factor capturing intra-sectoral structural change, AEEI the autonomous energy efficiency improvement and PIEEI the price-induced energy efficiency improvement.  
in which:
In the denominator, η is the end-use efficiency of energy carriers used in, for example, boilers and stoves, and MS represents the share of each energy carrier. Both population and economic activity levels are exogenous inputs into the model. Each of these factors is briefly discussed below:
*SE represents final energy;
• Structural change (SC). In each sector, the mix of activities changes as a function of development and time. These changes (referred to as structural change) may influence the energy intensity of a sector. For instance, using more private cars for transport instead of busses tends to lead to an increase in energy intensity. Historically, in several sectors, an increase in energy intensity can be observed, followed by a decrease. Evidence of this trend is more convincing in industry (with shifts from very basic  to heavy industry and, finally, to industries with high value-added products) than in others, such as  transport (historically, energy intensity has mainly been increasing in transport) (De Vries et al., 2001). Based on the above, in generic model formulation, we use a formulation in which energy intensity is driven by income, assuming first a peak in energy intensity, followed, finally, by a saturation of energy demand, at a constant per capita energy service level. In the calibration process, the choice of parameters may, for instance, lead to a peak in energy intensity higher than current income levels. In the technology-detailed energy demand submodels (see below), structural change is captured by other, equations that describe the underlying processes explicitly (e.g. modal shift in transport).  
*POP represents population;
*ACT/POP the sectoral activity per capita;
*[[HasAcronym::SC]] a factor capturing intra-sectoral structural change; *[[HasAcronym::AEEI]] the autonomous energy efficiency improvement;
*[[HasAcronym::PIEEI]] the price-induced energy efficiency improvement.
 
In the denominator,  
*η is the end-use efficiency of energy carriers used in, for example, boilers and stoves;
*MS represents the share of each energy carrier.  
 
Both population and economic activity levels are exogenous inputs into the model. Each of these factors is briefly discussed below:
#*Structural change (SC). In each sector, the mix of activities changes as a function of development and time. These changes (referred to as structural change) may influence the energy intensity of a sector. For instance, using more private cars for transport instead of busses tends to lead to an increase in energy intensity. Historically, in several sectors, an increase in energy intensity can be observed, followed by a decrease. Evidence of this trend is more convincing in industry (with shifts from very basic  to heavy industry and, finally, to industries with high value-added products) than in others, such as  transport (historically, energy intensity has mainly been increasing in transport) (De Vries et al., 2001). Based on the above, in generic model formulation, we use a formulation in which energy intensity is driven by income, assuming first a peak in energy intensity, followed, finally, by a saturation of energy demand, at a constant per capita energy service level. In the calibration process, the choice of parameters may, for instance, lead to a peak in energy intensity higher than current income levels. In the technology-detailed energy demand submodels (see below), structural change is captured by other, equations that describe the underlying processes explicitly (e.g. modal shift in transport).  
• Autonomous Energy Efficiency Increase (AEEI). This is a multiplier used in generic energy demand model to account for efficiency improvement that occurs as a result of technology improvement, independent of prices (in general, current appliances are more efficient than those available in the past). The autonomous energy efficiency increase for new capital is a fraction (f) of the economic growth rate based on the formulation of Richels et al. (2004). The fraction in TIMER varies between 0.45-0.30 (based on literature data) and is assumed to decline with time, as the scope for further improvement is assumed to decline. Although this efficiency improvement is assumed for new capital, the autonomous energy efficiency increase for the average capital stock is calculated as the weighted average value of the AEEI values of the total in capital stock, using a so-called vintage formulation. In the technology-detailed submodels, the autonomous energy efficiency increase is represented by improvement of individual technologies over time.  
• Autonomous Energy Efficiency Increase (AEEI). This is a multiplier used in generic energy demand model to account for efficiency improvement that occurs as a result of technology improvement, independent of prices (in general, current appliances are more efficient than those available in the past). The autonomous energy efficiency increase for new capital is a fraction (f) of the economic growth rate based on the formulation of Richels et al. (2004). The fraction in TIMER varies between 0.45-0.30 (based on literature data) and is assumed to decline with time, as the scope for further improvement is assumed to decline. Although this efficiency improvement is assumed for new capital, the autonomous energy efficiency increase for the average capital stock is calculated as the weighted average value of the AEEI values of the total in capital stock, using a so-called vintage formulation. In the technology-detailed submodels, the autonomous energy efficiency increase is represented by improvement of individual technologies over time.  
• Price-Induced Energy Efficiency Improvement (PIEEI). This is a multiplier used to describe the effect of rising energy costs in the form of induced investments in energy efficiency by consumers. In TIMER’s generic formulation it is included using an energy conservation cost curve. In the technology-detailed bottom-up submodels, it is represented by competing technologies with different efficiencies and costs.  
• Price-Induced Energy Efficiency Improvement (PIEEI). This is a multiplier used to describe the effect of rising energy costs in the form of induced investments in energy efficiency by consumers. In TIMER’s generic formulation it is included using an energy conservation cost curve. In the technology-detailed bottom-up submodels, it is represented by competing technologies with different efficiencies and costs.  

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